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http://www.cs.cmu.edu/~schneide/tut5/node42.html

A model evaluation method for training someone to read data. There are three methods: the holdout method, K-fold cross validation, and leave-one-out cross validation.

Proper citation: Cross Validation (RRID:SCR_000702) Copy   

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http://cran.r-project.org/web/packages/enviPick/

Software for sequential partitioning, clustering and peak detection of centroided LC-MS mass spectrometry data (.mzXML). Interactive result and raw data plot.

Proper citation: enviPick (RRID:SCR_003059) Copy   

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http://khmer.readthedocs.org/

Software library and suite of command line tools for working with DNA sequence that takes a k-mer-centric approach to sequence analysis. It is primarily aimed at short-read sequencing data such as that produced by the Illumina platform.

Proper citation: khmer (RRID:SCR_001156) Copy   

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http://www.ijs.si/ijsw/JSI

A scientific research institute that is involved with a broad spectrum of basic and applied research. Some of the areas they focus on include production and control technologies, communication and computer technology, and energy utilization and environmental science. They also engage in collaborations to develop medical equipment and new diagnostic methods into clinical medicine.

Proper citation: Jozef Stefan Institute; Ljubljana; Slovenia (RRID:SCR_000974) Copy   

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http://www.lead-dbs.org/

MATLAB toolbox for deep-brain-stimulation (DBS) electrode reconstructions and visualizations based on postoperative MRI and computed tomography (CT) imaging. The toolbox also facilitates visualization of localization results in 2D/3D, analysis of DBS-electrode placement's effects on clinical results, simulation of DBS stimulations, diffusion tensor imaging (DTI) based connectivity estimates, and fiber-tracking from the VAT to other brain regions (connectomic surgery).

Proper citation: LEAD-DBS (RRID:SCR_002915) Copy   

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https://pypi.python.org/pypi/illuminate/

Python module and utilities to parse the metrics binaries output by Illumina sequencers, and provides usable data in the form of python dictionaries and dataframes. Intended to emulate the output of Illumina SAV, it allows you to print sequencing run metrics to the command line as well as work with the data programmatically.

Proper citation: Illuminate (RRID:SCR_000178) Copy   

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http://clip.med.yale.edu/presto/

Software toolkit for processing raw reads from high-throughput sequencing of lymphocyte repertoires.

Proper citation: pRESTO (RRID:SCR_001782) Copy   

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http://www.ebi.ac.uk/Tools/

The European Bioinformatics Institute (EBI) toolbox area provides a comprehensive range of tools for the field of bioinformatics. These are subdivided into categories in the left menu for convenience. EBI has developed a large number of very useful bioinformatics tools. A few examples include: - Similarity & Homology - the BLAST or FASTA programs can be used to look for sequence similarity and infer homology. - Protein Functional Analysis - InterProScan can be used to search for motifs in your protein sequence. - Proteomic Services NEW - UniProt DAS server allows researchers to show their research results in the context of UniProtKB/Swiss-Prot annotation. - Sequence Analysis - ClustalW2 a sequence alignment tool. - Structural Analysis - MSDfold can be used to query your protein structure and compare it to those in the Protein Data Bank (PDB). - Web Services - provide programmatic access to the various databases and retrieval/analysis services EBI provides. - Tools Miscellaneous - Expression Profiler a set of tools for clustering, analysis and visualization of gene expression and other genomic data. Sponsors: This resource is sponsored by EBI.

Proper citation: Toolbox at the European Bioinformatics Institute (RRID:SCR_002872) Copy   

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http://www.edanzediting.com/journal_advisor

A training service which offers English editorial support for primarily non-native English speaking scientific researchers. Users answer a questionairre concerning their progress with their manuscript and Edanz suggests which of its services users should utilize. Services include Journal Advisor, English Editing, Expert Scientific Review, Cover Letter Development, Review Editing, Abstract Development, Response Letter Edit and Manuscript Check, Author-guided Rewriting, and Reviewer Recommendation. Edanz also offers training workshops and seminars for non-native English speakers.

Proper citation: Edanz's journal advisor (RRID:SCR_014013) Copy   

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http://www.gaworkshop.org/

The Genetic Analysis Workshops (GAWs) are a collaborative effort among genetic epidemiologists to evaluate and compare statistical genetic methods. For each GAW, topics are chosen that are relevant to current analytical problems in genetic epidemiology, and sets of real or computer-simulated data are distributed to investigators worldwide. Results of analyses are discussed and compared at meetings held in even-numbered years. The GAWs began in 1982 were initially motivated by the development and publication of several new algorithms for statistical genetic analysis, as well as by reports in the literature in which different investigators, using different methods of analysis, had reached contradictory conclusions. The impetus was initially to determine the numerical accuracy of the algorithms, to examine the robustness of the methodologies to violations of assumptions, and finally, to compare the range of conclusions that could be drawn from a single set of data. The Workshops have evolved to include consideration of problems related to analyses of specific complex traits, but the focus has always been on analytical methods. The Workshops provide an opportunity for participants to interact in addressing methodological issues, to test novel methods on the same well-characterized data sets, to compare results and interpretations, and to discuss current problems in genetic analysis. The Workshop discussions are a forum for investigators who are evolving new methods of analysis as well as for those who wish to gain further experience with existing methods. The success of the Workshops is due at least in part to the focus on specific problems and data sets, the informality of sessions, and the requirement that everyone who attends must have made a contribution. Topics are chosen and a small group of organizers is selected by the GAW Advisory Committee. Data sets are assembled, and six or seven months before each GAW, a memo is sent to individuals on the GAW mailing list announcing the availability of the GAW data. Included with the memo is a short description of the data sets and a form for requesting data. The form contains a statement to be signed by any investigator requesting the data, acknowledging that the data are confidential and agreeing not to use them for any purpose other than the Genetic Analysis Workshop without written permission from the data provider(s). Data are distributed by the ftp or CD-ROM or, most recently, on the web, together with a more complete written description of the data sets. Investigators who wish to participate in GAW submit written contributions approximately 6-8 weeks before the Workshop. The GAW Advisory Committee reviews contributions for relevance to the GAW topics. Contributions are assembled and distributed to all participants approximately two weeks before the Workshop. Participation in the GAWs is limited to investigators who (1) submit results of their analyses for presentation at the Workshop, or (2) are data providers, invited speakers or discussants, or Workshop organizers. GAWs are held just before the meetings of the American Society of Human Genetics or the International Genetic Epidemiology Society, at a meeting site nearby. We choose a location that will encourage interaction among participants and permit an intense period of concentrated work. The proceedings of each GAW are published. Proceedings from GAW16 were published in part by Genetic Epidemiology 33(Suppl 1), S1-S110 (2009) and in part by Biomed Central (BMC Proceedings, Volume 3, Supplement 7, 2009). Sponsors: GAW is funded by the Southwest Foundation for Biomedical Research.

Proper citation: Genetic Analysis Workshop (RRID:SCR_008350) Copy   

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http://hadoop.apache.org/

Software library providing a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures. The project includes these modules: * Hadoop Common: The common utilities that support the other Hadoop modules. * Hadoop Distributed File System (HDFS): A distributed file system that provides high-throughput access to application data. * Hadoop YARN: A framework for job scheduling and cluster resource management. * Hadoop MapReduce: A YARN-based system for parallel processing of large data sets.

Proper citation: Apache Hadoop (RRID:SCR_011879) Copy   

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http://neurogenetics.nia.nih.gov

A suite of web-based open source software programs for clinical and genetic study. The aims of this software development in the Laboratory of Neurogenetics, NIA, NIH are * Build retrievable clinical data repository * Set up genetic data bank * Eliminate redundant data entries * Alleviate experimental error due to sample mix-up and genotyping error. * Facilitate clinical and genetic data integration. * Automate data analysis pipelines * Facilitate data mining for genetic as well as environmental factors associated with a disease * Provide an uniformed data acquisition framework, regardless the type of a given disease * Accommodate the heterogeneity of different studies * Manage data flow, storage and access * Ensure patient privacy and data confidentiality/security. The GERON suite consists of several self contained and yet extensible modules. Currently implemented modules are GERON Clinical, Genotyping, and Tracking. More modules are planned to be added into the suite, in order to keep up with the dynamics of the research field. Each module can be used separately or together with others into a seamless pipeline. With each module special attention has been given in order to remain free and open to the academic/government user., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GERON (RRID:SCR_008531) Copy   

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https://www.nitrc.org/projects/gmac_2012/

Open-source software toolbox implemented multivariate spectral Granger Causality Analysis for studying brain connectivity using fMRI data. Available features are: fMRI data importing, network nodes definition, time series preprocessing, multivariate autoregressive modeling, spectral Granger causality indexes estimation, statistical significance assessment using surrogate data, network analysis and visualization of connectivity results. All functions are integrated into a graphical user interface developed in Matlab environment. Dependencies: Matlab, BIOSIG, SPM, MarsBar.

Proper citation: GMAC: A Matlab toolbox for spectral Granger causality analysis of fMRI data (RRID:SCR_009581) Copy   

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http://www.thomaskoenig.ch/Lester/ibaspm.htm

The aim of this work is to present a toolbox for structure segmentation of structural MRI images. All programs were developed in MATLAB based on a widely used fMRI, MRI software package, SPM99, SPM2, SPM5 (Wellcome Department of Cognitive Neurology, London, UK). Other previous works have developed a similar strategy for obtaining the segmentation of individual MRI image into different anatomical structures using a standardized Atlas. Have to be mentioned the one introduced by Montreal Neurological Institute (MNI) that merges the information coming from ANIMAL (algorithm that deforms one image (nonlinear registration) to match previously labelled) and INSECT (Cerebral Tissue Classification) programs for obtaining a suitable gross cortical structure segmentation (Collins et al, 1999). Here both, nonlinear registration and gray matter segmentation processes have been performed through SPM99, SPM2, SPM5 subroutines. Three principal elements for the labeling process are used: gray matter segmentation, normalization transform matrix (that maps voxels from individual space to standardized one) and MaxPro MNI Atlas. All three are combined to yield a good performance in segmenting gross cortical structures. The programs here can be used in general for any standardized Atlas and any MRI image modality. System Requirements: 1. The IBASPM graphical user interface (GUI) runs only under MATLAB 7.0 or higher. The non-graphical version runs under MATLAB 6.5 or higher. 2. Statistical Parametrical Mapping Software SPM2, SPM5 Main Functions: * Atlasing: Main function ( This file contains spm_select script from SPM5 toolbox and uigetdir script from MATLAB 7.0 ). * Auto_Labeling : Computes individual atlas. * Create_SPAMs : Constructs Statistical Probability Anatomy Maps (SPAMs). * Create_MaxProb : Creates Maximum Probability Atlas (MaxPro) using the SPAMs previously computed. * All_Brain_Vol : Computes whole brain volume masking the brain using the segmentation files (if the segmentation files does not exist it segments). * Struct_Vol : Computes the volume for different structures based on individual Atlas previously obtained by the atlasing process. * Vols_Stats : Computes mean and standard deviation for each structure in a group of individual atlases.

Proper citation: IBASPM: Individual Brain Atlases using Statistical Parametric Mapping Software (RRID:SCR_007110) Copy   

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http://www.fetk.org/

The Finite Element ToolKit (FETK) is a collaboratively developed, evolving collection of adaptive finite element method (AFEM) software libraries and tools for solving coupled systems of nonlinear geometric partial differential equations (PDE). The FETK libraries and tools are written in an object-oriented form of ANSI-C and in C , and include a common portability layer (MALOC) for all of FETK, a collection of standard numerical libraries (PUNC), a stand-alone high-quality surface and volume simplex mesh generator (GAMer), a stand-alone networked polygon display tool (SG), a general nonlinear finite element modeling kernel (MC), and a MATLAB toolkit (MCLite) for protyping finite element methods and examining simplex meshes using MATLAB. The entire FETK Suite of tools is highly portable (from iPhone to Blue Gene/L), thanks to use of a small abstraction layer (MALOC) and heavy use of the GNU Autoconf infrastructure. The primary FETK ANSI-C software libraries are: :- MALOC is a Minimal Abstraction Layer for Object-oriented C/C programs. :- PUNC is Portable Understructure for Numerical Computing (requires MALOC). :- GAMer is a Geometry-preserving Adaptive MeshER (requires MALOC). :- SG is a Socket Graphics tool for displaying polygons (requires MALOC). :- MC is a 2D/3D AFEM code for nonlinear geometric PDE (requires MALOC; optionally uses PUNC GAMER SG). Application-specific software designed for use with the FETK software libraries is: :- GPDE is a Geometric Partial Differential Equation solver (requires MALOC PUNC MC; optionally uses GAMER SG). :- APBS is an Adaptive Poisson-Boltzmann Equation Solver (requires MALOC PUNC MC; optionally uses GAMER SG). :- SMOL is a Smoluchowki Equation Solver solver (requires MALOC PUNC MC; optionally uses GAMER SG). MATLAB toolkits designed for use with MC and SG or as standalone packages: :- MCLite is a simple 2D MATLAB version of MC designed for teaching. :- FEtkLAB is a sophisticated 2D MATLAB adaptive PDE solver built on top of MCLite. Related packages developed and maintained by FETK developers (included in PUNC above): :- PMG is a Parallel Algebraic MultiGrid code for general semilinear elliptic equatons. :- CgCode is a package of Conjugate gradient Codes for large sparse linear systems. Sponsors: This resource is developed and supported by the MCP Research Group at the UCSD Center for Computational Mathematics.

Proper citation: Finite Element Toolkit (RRID:SCR_008682) Copy   

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http://www.bcgsc.ca/platform/bioinfo/software/alea

A computational software toolbox for allele-specific (AS) epigenomics analysis. It incorporates allelic variation data within existing resources, allowing for the identification of significant associations between epigenetic modifications and specific allelic variants in human and mouse cells. It provides a customizable pipeline of command line tools for AS analysis of next-generation sequencing data (ChIP-seq, RNA-seq, etc.) that takes the raw sequencing data and produces separate allelic tracks ready to be viewed on genome browsers. ALEA takes advantage of the available genomic resources for human (The 1000 Genomes Project Consortium) and mouse (The Mouse Genome Project) to reconstruct diploid in-silico genomes for human or hybrid mice under study. Then, for each accompanying ChIP-seq or RNA-seq dataset, it generates two Wiggle track format (WIG) files from short reads aligned differentially to each haplotype.

Proper citation: ALEA (RRID:SCR_006417) Copy   

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http://www.nitrc.org/projects/gppi/

An automated toolbox for a generalized form of psychophysiological interactions for SPM and FSFAST. The automated toolbox can do the following: (a1) produce identical results to the current implementation in SPM (a2) use the current implementation of PPI in SPM but using the regional mean instead of the eigenvariate (a3) use a generalized form that allows a PPI for each task to be in the same model using either the regional mean of eigenvariate (b) create the model using the output of one of the (a) options and the first level design (c) estimate the model (/results directory) (d) compute the contrasts specified.

Proper citation: Generalized PPI Toolbox (RRID:SCR_009489) Copy   

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http://www.vivo.colostate.edu/molkit/index.html

The Molecular Toolkit is a group of programs for analysis and manipulation of nucleic acid and protein sequence data. The programs are written in Java (1.0) and require that your browser support this language. Also, it''s best if your monitor supports a screen resolution of at least 800x600. Nucleic Acid Analysis and Manipulation Programs: *Dot Plots - Examine the similarity of two DNA (or RNA) sequences by production of a similarity matrix displayed as a dot plot. *Manipulate and Display Sequences - Perform simple manipulations on a DNA sequence (inverse, complement, inverse-complement, double-stranded etc). *Restriction Maps - Generate graphical and text-based maps for restriction endonuclease cleavage of DNA. *Translate - Translate a DNA or RNA sequence and obtain graphical and text depictions of the resulting protein sequences. Protein Analysis Programs *Reverse Translate - Reverse translate a protein sequence into DNA. *Protein Composition - Obtain the amino acid composition of a protein. *Hydrophobicity Plots - Plot hydrophobic and hydrophilic domains of a protein.

Proper citation: Molecular Toolkit (RRID:SCR_007068) Copy   

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http://www.nitrc.org/projects/dti-denoising/

A Matlab package which contains six denoising filters and a noise estimation method for 4D DWI. The package includes nonlocal means, local PCA and Oracle DCT methods. Based on image redundancy and/or sparsity, the proposed filters provide efficient denoising while preserving fine structures.

Proper citation: DTI denoising (RRID:SCR_014102) Copy   

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http://www.rdkit.org/

An open-source cheminformatics and machine-learning toolkit that is useable from Java or Python. It includes a collection of standard cheminformatics functionality for molecule I/O, substructure searching, chemical reactions, coordinate generation (2D or 3D), fingerprinting, etc., as well as a high-performance database cartridge for working with molecules using the PostgreSQL database. Documentation is available on the main website.

Proper citation: RDKit: Open-Source Cheminformatics Software (RRID:SCR_014274) Copy   

•   Source: SciCrunch Registry